Assessing long-term effects of gaseous air pollution exposure on mortality in the United States using a variant of difference-in-differences analysis

Long-term mortality effects of particulate air pollution have been investigated in a causal analytic frame, while causal evidence for associations with gaseous air pollutants remains extensively lacking, especially for carbon monoxide (CO) and sulfur dioxide (SO2). In this study, we estimated the causal relationship of long-term exposure to nitrogen dioxide (NO2), CO, SO2, and ozone (O3) with mortality. Utilizing the data from National Morbidity, Mortality, and Air Pollution Study, we applied a variant of difference-in-differences (DID) method with conditional Poisson regression and generalized weighted quantile sum regression (gWQS) to investigate the independent and joint effects. Independent exposures to NO2, CO, and SO2 were causally associated with increased risks of total, nonaccidental, and cardiovascular mortality, while no evident associations with O3 were identified in the entire population. In gWQS analyses, an interquartile range-equivalent increase in mixture exposure was associated with a relative risk of 1.067 (95% confidence interval: 1.010–1.126) for total mortality, 1.067 (1.009–1.128) for nonaccidental mortality, and 1.125 (1.060–1.193) for cardiovascular mortality, where NO2 was identified as the most significant contributor to the overall effect. This nationwide DID analysis provided causal evidence for independent and combined effects of NO2, CO, SO2, and O3 on increased mortality risks among the US general population.


Section 2 Supplementary tables & figures
Table S1 Risks estimates (with 95% CIs) of total and cause-specific mortality associated with each IQR increase in NO2, SO2, O3, and CO using single-, bi-, tri-and quadpollutant models.
Table S2 Risks of total and cause-specific mortality associated with each IQR increase in individual gaseous pollutant or IQR-equivalent rise in mixture exposure, estimated by copollutant analyses by additionally adjusting for PM10.
Table S3 Age-stratified associations between long-term exposure to gaseous air pollutants and mortality outcomes.

Causal analytical frame
Causality is a generic relationship between an effect and a cause that produces it.This relationship should be precisely established in the context of physiological mechanisms, rather than solely relying on statistical analysis.In the realm of environmental epidemiology, the predominant approach has traditionally involved modeling associations, typically by regressing observed outcomes against linear or nonlinear functions of observed covariates.However, describing associations between exposures and outcomes generally does not suffice, and an assessment of causal effects is needed 1 .Causal effects refer to the effects that would be seen under experimental changes of exposures.By causal inference we mean the process of inferring causal effects from data 2 .Hence, a causal inference framework should be considered when analyzing the impact of environmental exposures on health outcomes.Such a causal inference framework is a set of statistical frameworks for analyzing causal relationships between exposures and outcomes based on counterfactual estimation of observational data.Three analytical frameworks have been proposed, namely the counterfactual framework, the latent outcome model and the structural causal model.

Conditional Poisson regression
Conditional Poisson regression serves as a pivotal tool used to model count data and contingency tables.Poisson regression assumes the response variable Y has a Poisson distribution, and assumes the logarithm of its expected value can be modeled by a linear combination of unknown parameters.Conditional Poisson models emerge as an invaluable counterpart to unconditional ones, adept at accommodating overdispersion and autocorrelation of time-series data 3 .

Generalized weighted quantile regression
Generalized weighted quantile regression (gWQS) is a convenient tool for solving the problem of high dimensionality and high correlation between multiple pollutants, especially homologous pollutants 4 .The method produces results with good interpretability and effectively identifies high-risk factors.The principle of the gWQS regression model is to combine the effects of multiple factors into a single index by means of interquartile spacing and weighting, and then to perform regression analyses.The assigned weights reflecting the magnitude of influence exerted by different factors on outcomes 5 .

Section 2 Supplementary tables & figures
Table S1 Risks estimates (with 95% CIs) of total and cause-specific mortality associated with each IQR increase in NO2, SO2, O3, and CO using single-, bi-, tri-and quad-pollutant models.

Fig. S2
Fig. S2 Spearman correlation coefficients between air pollutants in 108 US cities for the period 1987−2000.

Table S2
Risks of total and cause-specific mortality associated with each IQR increase in individual gaseous pollutant or IQR-equivalent rise in mixture exposure, estimated by copollutant analyses by additionally adjusting for PM10.

Table S3
Age-stratified associations between long-term exposure to gaseous air pollutants and mortality outcomes.